A Novel Starting-point-independent Wavelet Coefficient Shape Matching

In many computer vision tasks,in order to improve the accuracy and robustness to the noise,wavelet analysis is preferred for its natural multi-resolution property.However,the wavelet representation suffers from the dependency on the starting point in shape matching.For overcoming the problem,the Zernike moments are introduced,and a novel Starting-Point-Independent wavelet coefficient shape matching algorithm is presented.The proposed matching algorithm firstly gains the object contours,and gives the translation and scale invariant object shape representation.The object shape representation is converted to dyadic wavelet representation by wavelet transform,and then the Zernike moments of wavelet representation in different scales are calculated.With respect to property of rotation invariant of Zernike moments,consider the Zernike moments as the feature vector to calculate the similarity between the object and template image,which overcoming the problem of dependency on starting point.The experimental results indicates that the proposed algorithm is efficient,precise,and robust.